GenAI in Telcommunications

• LLM as Intelligent router
• LLM as Universal translator
• NetOrchLLM Framework
  • LLM acts as coordinator, understanding intent and picking the right expert model.
  • Model repository holds the trusted analytical functions for wireless tasks.
  • Heavy computation is delegated to these specialized models, not the LLM.
• In essence : Unstructured to structured repsentation translator

Traditional Wireless System (Without LLM)

flowchart TD
    A["Raw Sensory Data<br/>(GPS, Camera, LiDAR, Radio)"] --> B["Preprocessing/Feature Extraction"]
    B --> C["Math/ML-based Algorithm<br/>(Beam Tracking, V2X Decisions, etc.)"]
    C --> D["Structured Output/Action<br/>(Beam Angle, Network Command)"]
    D --> E["Wireless System Actuation or V2X Application"]

LLM-Based Wireless System (ENWAR/NetOrchLLM Style)

flowchart TD
    A["Raw Sensory Data<br/>(GPS, Camera, LiDAR, Radio)"] --> B[Textualization<br/>Multi-Modal Embedding]
    B --> C["RAG/Knowledge Retrieval<br/>+ Context Fusion"]
    C --> D["LLM Reasoning<br/>(Interpret + Explain + Decide)"]
    D --> E["Convert Decision<br/>to Structured Action"]
    E --> F["Math/ML-based Algorithm<br/>(Precise Calculation)"]
    F --> G["Structured Output/Action<br/>(Beam Angle, Network Command)"]
    G --> H[Wireless System Actuation or V2X Application]

• 3 Agents (Router, Resolver, Translator)
• Each agent backed by KB store.
• Actual deployment is still by traditional tools

Highlights

• Highlevel control is fixed
• Handle unstructed inputs

Returning theme

• Universal Translator / Router
• Unstructured -> Sturctured

• 2 Agents (Match & CodeGen)
• Match
  • Maps user intent to closest interface (API, Func call etc)
  • Instructs CodeGen to create wrappers
• CodeGen
  • Generate working code

Highlights

• Instant Vendor interoperablity
• On-Demand system integration
• Spec to Code - a reality